added mixpeek video embedding support

qdrant-landing/content/documentation/embeddings/mixpeek.mdx

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+# Mixpeek Video Embedding with Qdrant
+
+This page provides instructions on how to integrate Mixpeek video embedding with the Qdrant vector database. 
+Mixpeek's video processing capabilities allow you to chunk and embed videos, while Qdrant provides efficient storage and retrieval of these embeddings.
+
+## Prerequisites
+
+- Python 3.7+
+- Mixpeek API key
+- Mixpeek client installed (`pip install mixpeek`)
+- Qdrant client installed (`pip install qdrant-client`)
+
+## Installation
+
+1. Install the required packages:
+
+```bash
+pip install mixpeek qdrant-client
+```
+
+2. Set up your Mixpeek API key:
+
+```python
+from mixpeek import Mixpeek
+
+mixpeek = Mixpeek('your_api_key_here')
+```
+
+3. Initialize the Qdrant client:
+
+```python
+from qdrant_client import QdrantClient
+
+client = QdrantClient("localhost", port=6333)
+```
+
+## Usage
+
+### 1. Process and Embed Video
+
+First, process the video into chunks and embed each chunk:
+
+```python
+from mixpeek import Mixpeek
+from qdrant_client import QdrantClient, models
+
+mixpeek = Mixpeek('your_api_key_here')
+client = QdrantClient("localhost", port=6333)
+
+video_url = "https://mixpeek-public-demo.s3.us-east-2.amazonaws.com/starter/jurassic_park_trailer.mp4"
+
+# Process video chunks
+processed_chunks = mixpeek.tools.video.process(
+    video_source=video_url,
+    chunk_interval=1,  # 1 second intervals
+    resolution=[720, 1280]
+)
+
+# Embed each chunk and insert into Qdrant
+for index, chunk in enumerate(processed_chunks):
+    print(f"Processing video chunk: {index}")
+
+    embedding = mixpeek.embed.video(
+        model_id="vuse-generic-v1",
+        input=chunk['base64_chunk'],
+        input_type="base64"
+    )['embedding']
+
+    # Insert into Qdrant
+    client.upsert(
+        collection_name="video_chunks",
+        points=[models.PointStruct(
+            id=index,
+            vector=embedding,
+            payload={
+                "start_time": chunk["start_time"],
+                "end_time": chunk["end_time"]
+            }
+        )]
+    )
+
+    print(f"  Embedding preview: {embedding[:5] + ['...'] + embedding[-5:]}")
+
+print(f"Processed and inserted {len(processed_chunks)} chunks")
+```
+
+### 2. Search for Similar Video Chunks
+
+To search for similar video chunks, you can use either text or video queries:
+
+#### Text Query
+
+```python
+query_text = "a car chase scene"
+
+# Embed the text query
+query_embedding = mixpeek.embed.video(
+    model_id="vuse-generic-v1",
+    input=query_text,
+    input_type="text"
+)['embedding']
+
+# Search in Qdrant
+search_results = client.search(
+    collection_name="video_chunks",
+    query_vector=query_embedding,
+    limit=5
+)
+
+for result in search_results:
+    print(f"Chunk ID: {result.id}, Score: {result.score}")
+    print(f"Time range: {result.payload['start_time']} - {result.payload['end_time']}")
+```
+
+#### Video Query
+
+```python
+query_video_url = "https://mixpeek-public-demo.s3.us-east-2.amazonaws.com/starter/jurassic_bunny.mp4"
+
+# Embed the video query
+query_embedding = mixpeek.embed.video(
+    model_id="vuse-generic-v1",
+    input=query_video_url,
+    input_type="url"
+)['embedding']
+
+# Search in Qdrant
+search_results = client.search(
+    collection_name="video_chunks",
+    query_vector=query_embedding,
+    limit=5
+)
+
+for result in search_results:
+    print(f"Chunk ID: {result.id}, Score: {result.score}")
+    print(f"Time range: {result.payload['start_time']} - {result.payload['end_time']}")
+```
+
+## Note on Collection Creation
+
+Make sure to create a Qdrant collection before inserting vectors. You can create a collection with the appropriate vector size (768 for "vuse-generic-v1" model) using:
+
+```python
+client.create_collection(
+    collection_name="video_chunks",
+    vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE)
+)
+```
+
+## Resources
+For more information on Mixpeek Embed, review the official documentation: https://docs.mixpeek.com/api-documentation/inference/embed